Entrepreneurial learning in online communities

Entrepreneurial learning is a key ingredient in the entrepreneurial process as it allows entrepreneurs to improve their venture, product, and skills, and enables them to learn and possibly recover from failure (Cope, 2005, 2011). Entrepreneurial learning is also a key part of the effectuation process that drives venture creation (Sarasvathy, 2001). In framing and understanding entrepreneurial learning, research has drawn on various theories. Initially, experiential learning emerged as the dominant theory on individual learning of entrepreneurs (Wang & Chugh, 2014). Experiential learning theory defines learning as “the process whereby knowledge is created through the transformation of experience” (Kolb, 1984, p. 41). The theory outlines learning as a process of taking in, interpreting, making sense of, and acting upon information. Kolb (1984) imagines this as an idealized learning cycle where the learner continuously forms and reforms ideas and solutions via action, experience, and reflection. The theory of experiential learning has been applied in a multitude of contexts. For example, Corbett (2005) builds on it to differentiate how entrepreneurs identify and exploit opportunities, Politis and Gabrielsson (2009) employ it to understand how entrepreneurs learn from failure, and Pittaway and Cope (2007) draw from it to evaluate entrepreneurial education.

While experiential learning has brought many insights into the study of entrepreneurial learning, it has been criticized for insufficiently explaining how individuals learn with and from each other, since it mostly focuses on psychological processes and not on sociological ones (Holman et al., 1997; Kayes, 2002). Studies in entrepreneurial learning have, therefore, put a stronger focus on the social embeddedness of learning (Hamilton, 2011; Zozimo et al., 2017), building upon vicarious learning. In opposite to experiential learning, in vicarious learning, the learner draws from observing the behavior and its consequences from a model instead of learning from own performance outcomes (Gioia & Manz, 1985). Examples of such research are Zozimo et al.’s (2017) study of how entrepreneurs learn from observing role models, Pittaway et al.’s (2015) work on learning in student clubs, and Hamilton’s (2011) study of how family businesses can function as communities of practice, where entrepreneurs learn from their family how to run a business. This version of vicarious learning theory has also been used to understand how employees in organizations gain knowledge from online communities (Hwang et al., 2015; Kane, 2017; Leonardi, 2014, 2017). For example, Leonardi (2014) analyzes how an online community serves as a knowledge-sharing device, which allows employees to share knowledge more easily through a virtual space than a physical space.

Although vicarious learning has provided novel insights into how entrepreneurs learn from each other and how individuals can gain knowledge from engaging in online communities, it has recently come under scrutiny from scholars pointing out serious limitations. Myers (2018, 2020) argues that vicarious learning simplifies learning into stable, unidirectional learning relationships between an expert and a novice. In this learning relationship, the expert simply transfers their knowledge to the recipient, the novice. It represents a troublesome simplification because individuals do not learn through “consuming” others’ experience, but through a “give and take” process, in which experience is analyzed and discussed (Myers, 2018, 2020). Therefore, classic vicarious learning theory limits our understanding of learning in communities (Myers, 2020) and, in particular, in online communities, in which learning is facilitated by active knowledge creation through conversations (Faraj et al., 2016; Myers et al., 2018). Classic vicarious learning theory is simply not able to conceptualize learning as socially constructed through conversations (Baker et al., 2005; Faraj et al., 2016; Kolb & Kolb, 2005; Myers, 2018), making it ineffective in understanding the social interactions in online communities that create knowledge and facilitate learning (Faraj et al., 2016). In order to investigate how entrepreneurs learn in online communities, we draw on coactive vicarious learning (Myers, 2018), which focuses on learning as a social process that takes place through conversations. Hereby, we are taking a sociological approach nested in symbolic interactionism (Blumer, 1986; Myers, 2018) in contrast to previous research on entrepreneurial learning favoring a cognitive focus (Grégoire et al., 2011).

Myers (2018) shifts vicarious learning towards a more relational process that occurs coactively. Myers (2018, p. 613f) defines coactive vicarious learning as “a discursive learning process where individuals (i.e., a model and learner) intentionally share and jointly process a model’s work experience(s) in interpersonal interactions to coconstruct an emergent, situated understanding of the experience(s).” By taking into account the exchanges, relationships, and knowledge sharing that define online communities (Faraj et al., 2016), Myers’ theory is well fitting to capture and understand entrepreneurial learning processes in online communities. For example, Myers et al. (2018) use the theory to understand how surgeons use social media to learn as it allows them “to discursively react to one another’s ideas and coconstruct a more robust, detailed understanding of their experiences” (Myers et al., 2018, p. 235).

Myers (2018) outlines three key discursive elements that constitute coactive vicarious learning interactions: experience, analysis, and support. Coactive vicarious learning unfolds as individuals share and gain access to a greater number of experiences, which function as a basis for reflection and the development of knowledge. For instance, through storytelling, groups can share, compare, and build on each other’s experiences, allowing them to create new shared knowledge in the process. Coactive vicarious learning further unfolds through the analysis of shared and co-constructed experiences, which allows members to “evaluate, reinterpret, or compare their emerging understanding of the experience” (Myers, 2018, p. 618). Such analysis can unfold through probing, asking for clarification or criticism. Last, support by the community enhances an individual’s learning by creating safety and allowing them to develop beliefs and relationships in the community. For instance, friendship communities foster the transmission of more tacit and intimate knowledge. These three elements shape the learning context in critical ways; for example, a context rich in support may facilitate deeper conversations about failure and learning from such critical events (Myers, 2018, p. 618).

The coactive vicarious learning perspective is promising because it could help improve our understanding of entrepreneurial learning as a socially embedded process situated in communities, a perspective that has received little attention in the literature (Hamilton, 2011; Toutain et al, 2017; Wang & Chugh, 2014). Consequently, we seek to explore how experience, analysis, and support—as key elements of co-active vicarious learning—manifest in online conversations among entrepreneurs and how these conversations differ in dynamics and style.

In order to gain insight into how entrepreneurs use online communities as a space for knowledge creation, we collected data from an online community of entrepreneurs (“r/startups”) on Reddit. The community consists of 382,000 members with several hundred commenters being online at any given time. The community space is public and openly accessible—even to users without a registered account—and there is no expectation of privacy among the members (Sugiura et al., 2017). Commenters are anonymous and identify with a chosen online handle. The community is dedicated to “discussing startup problems and solutions.” As emphasized by the moderators, the purpose of the community is to “support others, educate others, inspire others and foster authentic relationships.”

In our sampling, we strived to select threads that had the best chance to answer our research question (Lincoln & Guba, 1985). To this end, we compared daily threads (all threads) as well as “top-rated” threads (most “upvoted” threads). While both types of threads contained similar themes, the daily threads usually had much less activity than the top threads. Following a purposeful sampling logic (Lincoln & Guba, 1985; Patton, 2002), we collected the top 100 threads within a span of 12 months (October 2018–October 2019) with all their respective comments, resulting in a total corpus of 10,277 comments. The longitudinal data was scraped via the Python Reddit API wrapper (PRAW). Reddit defines the success of community threads based on the number of “upvotes” received by community members. The selected top-rated threads received between 794 and 113 upvotes, and they encompass between 625 and 10 comments each. In gathering successful threads over a longer period, we preclude short-term spikes or trends in the conversation from distorting the overall sample and provide often-read and representative threads (Levina & Vaast, 2015; McKenna et al., 2017).

To analyze the data, we relied on a mix of context-driven inductive and theory-driven structured coding processes (Glaser & Strauss, 1967; Miles, Huberman, & Saldaña, 2014). Such ways of analyzing data from online communities have gained prominence in recent years (Levina & Vaast, 2015; McKenna et al, 2017; Vaast et al, 2017). Our data analysis encompasses four steps. First, in order to familiarize ourselves with the online community environment, all researchers observed the online community daily between September 2019 and October 2019, creating an inventory of recurring conversation dynamics, themes, language, and community-specific idiosyncrasies. In a second step, we collected, categorized, and clustered the top 100 threads—consisting of post title and post text—of the past 12 months according to the type of learning conversation.¹ In the descriptive analysis, we built clusters of conversations according to observed conversation dynamics (e.g., advice-seeking, lessons learned, call to share, and providing resources) as well as conversation content (e.g., self-reflection, strategy advice, and interpersonal conflict). The initial clustering revealed five distinct conversation dynamics: lessons learned, advice-seeking, call to share, reflections, as well as tips, tricks, and resources² (see Fig. 1). In these two first steps, we sought to structure our data following an inductive approach (Glaser & Strauss, 1967).

In our third step, we sought to build theory on how entrepreneurs interact within the online community to construct knowledge. To this end, we used Myers’ (2018, pp. 617–618) analytical framework and described and compared each of the identified conversation clusters with respect to the three discursive elements of coactive vicarious learning—experience, analysis, and support. We compared the conversations with respect to (1) how experiences were shared within the community and as a back-and-forth between posters and commenters, (2) how experiences were collectively analyzed, as well as (3) how community members socially and emotionally supported each other during the knowledge building.

The analysis of online conversations further revealed two specific patterns: learning conversations across categories differed not just with respect to conversation dynamics and discursive elements but also with respect to their emotionality (tone and language of conversations range from technical to emotional) and their level of abstraction (framing of conversations ranges from concrete to abstract). Emotionality effectively deepens the discursive element of support (technical vs. emotional support) while the level of abstraction primarily adds nuance to the discursive element of analysis (concrete vs. abstract frame of analysis). To shed further light on these two patterns, we decided to include an additional fourth step of data analysis by coding all 100 threads for emotionality (technical = 1 vs. emotional = 2) as well as level of abstraction (low = 1, medium = 2, high = 3). While the level of abstraction inductively emerges as a dimension from the data, our understanding of the dimension is aligned with Trope and Liberman’s (2010) work on construal levels. Through this last step or analysis, we identified four “micro-learning” spaces, which differed in terms of emotionality and abstraction, thereby indicating heterogeneous learning contexts. To secure the credibility of our findings (Pratt et al., 2020), we applied investigator triangulation (Denzin, 1970). All authors independently coded all threads and subsequently discussed diverging cases (11 in total) until a consensus was reached.

Our results further indicate that entrepreneurial learning within the online community takes place against the backdrop of four specific micro-learning contexts (see Fig. 2), marked by varying degrees of emotionality and levels of abstraction (construal levels). We found that the identified learning themes unfold in (1) a classroom context (high abstraction, low emotion), (2) a collab context (low abstraction, low emotion), (3) a club context (high abstraction, high emotion), as well as (4) a care context (low abstraction, high emotion).

The second micro-learning context works akin to a collaborative or “collab-space” where entrepreneurs meet to discuss and build concrete and often skill-based knowledge by exchanging resources, guidelines, and action-oriented lessons that they have gathered during their entrepreneurial tenure. Tone and language are collegial but not overly emotional; members of collab spaces often discuss on eye level without a discernable hierarchy and without a strong focus on the initial poster. Most posts are met with a brief thank you and a follow-up question or with additional knowledge or experiences. Collab spaces span several different entrepreneurial learning conversations such as lessons learned, calls to share, as well as tips, tricks, and resources. While the collab space is an active environment for technical and even “nerdy” debates, there are no discernible entry barriers and entrepreneurs of varying levels of experience and maturity feel comfortable sharing their own experience and contributing with comments or questions. When asked why an experienced user with “20 years bouncing between being a consultant, a [venture capitalist] and an entrepreneur” would take the time to share their insight with their less experienced counterparts, they reply: “I’m a big believer in spontaneous serendipity […] If I can be that […] for someone, then I’m thrilled. This start-up world is hard. We need as many good people helping each other as we can get.”

The third micro-learning context works like a society or a club space. In this space, “members” evaluate, interpret, and compare medium- to high-level learnings or questions which are often derived from personal experiences such as entrepreneurial success or failure. As such, the evolving discussions can be emotional and even personal in nature. The club space is primarily a background for advice-seeking and higher-level reflections. In particular, members use club spaces to discuss interpersonal or strategy issues that they have personally encountered. The club space is marked by rather high participation thresholds and in-community hierarchies with members regularly offering their “credentials” in the form of experience, tenure, or job title as a precursor to their contributions. Unlike in the collab space, answers offered in the club space are often questioned and challenged by other members. One user who seeks advice on coping with their “lying CEO” receives multiple and sometimes conflicting pieces of advice that are fiercely discussed within the community. While some advise the user to “leave [the company] ASAP,” others interject that “it is a bit soon to advise [the original poster] to leave the company.” While some say to inform investors immediately, others warn not to contact investors under any circumstances before the [original poster] has “lawyered up.”

The final micro-learning context can be characterized as a highly informal and personal care space where members take over roles of confidantes or friends. Here, the issues discussed are tangible and concrete, encompassing everyday events or states of mind. Care spaces span multiple learning conversations, such as reflections—often expressed as short “rants” about current personal predicaments—or personal lessons learned. Conversations within care spaces are marked by a highly emotional language and tone. Here, emotionality is often conveyed through the use of emotional verbs (love, like, hate, etc.), adjectives (happy, sad, afraid, desperate, depressed, etc.), or nouns (stress, burnout, anxiety) as well as the use of punctuation to create “emoticons” such as smiley faces or multiple exclamation marks. Further emotionality cues are derived from personal terms of endearment such as “friend,” “buddy,” or “brother”/ “sister.” Care spaces are “no-threshold” zones where all users are welcome and validated—both as original contributors and as commenters. As such, care spaces are especially inviting environments for “cathartic rants” as one user puts it. Unlike in the classroom or collab space where the quality of content and generalizability is a central concern, the care space allows for purely introspective and individual reflection: “It’s like burnt rubber in your chest. […]I don’t want to do this. I hate calling people. I hate sales. I hate bothering people. […] I hate the way my voice trembles on the phone. I hate the way my ribcage feels frozen when I’m waiting for people to pick up the phone.” Replies and comments within the care space all follow a pattern of non-judgment and validation where users either echo the sentiment (“Bro…I know that feeling”) or offer advice (“it’s time to hire an assistant to do the cold calling for you”) or a word of courage or support (“Gorgeous rant! I hope it was cathartic for you. It was for me.”).

As our findings indicate that entrepreneurial learning occurs both in different conversations that encompass various topics and in different micro-learning contexts, which present different ways of coactive vicarious learning. Therefore, the way a particular conversation unfolds may be markedly different, depending on the micro-learning context it takes place in.

Building on coactive vicarious learning, our findings provide novel insights into the nascent research on how entrepreneurs learn as part of communities in general (e.g.Hamilton, 2011; Zozimo et al., 2017) and online communities in particular (Faraj et al., 2016). We extend the literature on learning in communities by highlighting how engaging in online communities may enhance critical self-reflection and higher-level learning (e.g., Cope, 2003, 2005) and how online communities may differ in their requirements for participation compared to other communities (Hamilton, 2011; Konopaski et al., 2015).

First, our findings suggest that entrepreneurs in online communities learn from sharing and analyzing experiences of critical events such as failures (e.g., a venture does not scale), disruptions (e.g., co-founder leaves venture), or conflicts (e.g., suspected employee misconduct). Such learning is particularly present in conversations surrounding “advice-seeking,” “lessons learned,” and “reflections.” This closely corresponds to Cope (2003, p. 445) who emphasizes that “non-routine events represent a key entrepreneurial learning mechanism” as they spark critical self-reflection that leads to distinctive forms of fundamental “higher-level learning” (as opposed to routine events which stimulate more incremental lower-level learning). Our findings highlight that online communities may be a powerful catalyst for such critical reflection and higher-level learning. On the one hand, an entrepreneur who experiences a discontinuous event might find an online community where their own experience and behavior are mirrored, commented on, questioned, and exposed by other entrepreneurs to be particularly conducive for critical reflection and higher-level learning. On the other hand, an entrepreneur who has not (yet) experienced a specific discontinuous event could still be peripherally exposed to such fundamental learning experiences and thus vicariously observe critical self-reflection and higher-level learning, which may help in navigating such critical events in the future. Thus, online communities, as anonymous conversation spaces, may provide particularly immediate, intimate, and unfiltered insights into how others have experienced, analyzed, and dealt with discontinuous events.

By shedding light on entrepreneurial learning in the socially embedded context of online communities, our research contributes further to Cope’s (2005, p. 385) call for “more research on distinctive forms of learning that arise from the entrepreneur’s engagement in social relationships.” While Cope (2005) suggests looking towards family and domestic partners as “sounding boards” for entrepreneurial learning, we propose to complement the perspective with specialized online communities, such as Reddit. Against this background, we encourage future research to scrutinize higher vs. lower level learning in online communities, as well as to investigate how and across which conversations critical self-reflection and higher-level learning occur in online communities (Cope, 2003). To understand such processual phenomena, we encourage future research to engage in longitudinal netnographies (Kozinets, 2010) or other processual methods.

Second, whereas previous research illustrates how entrepreneurs learn through deep engagement and socialization into a community, such as a family business (Hamilton, 2011; Konopaski et al., 2015), our findings indicate that in most cases, socialization plays a secondary role in online communities such as Reddit. Online community members can seamlessly access and exit conversations and thereby engage in coactive vicarious learning, without having to invest extensive time legitimizing their presence. It does not mean that online communities are complete without legitimacy requirements. For instance, in conversations surrounding the “lessons learned” paradigm, entrepreneurs generally have to signal credentials, such as tenure, knowledge, or experience of critical events. Future research may engage more with access thresholds and legitimacy requirements across different communities and how they may affect learning dynamics.

Our finding that the online community consists of four separate micro-learning contexts extends our knowledge on online communities as social spaces for different conversations. Levina and Arriaga (2014, p. 477) argue that online communities should be seen as a “social space engaging agents in producing, evaluating, and consuming content online that is held together by a shared interest and a set of power relations among agents sharing this interest.” We extend such theorization by providing new insights indicating that online communities do not consist of a singular social space, but multiple, heterogeneous spaces—the micro-learning contexts—in which social interactions differ. For example, conversations among entrepreneurs differ in terms of the emotionality shown. This is important for two reasons. First, in highlighting these different spaces, we provide an alternative to the network-based analysis of online communities (e.g., Johnson et al., 2015). Our model allows researchers to distinguish an online community by how interactions create certain spaces, rather than by who is connected. Thereby, the model can facilitate research into the knowledge creation in online communities, which is inhibited by network analysis (Faraj et al., 2016).

Second, our findings on micro-learning contexts connect to the growing recognition of the importance of the social and relational context around learning activities (Politis et al., 2019). For example, Pugh et al. (2021) show how universities can foster reflective and interactive cultures where participants actively engage to share and analyze experiences, which helps build up local entrepreneurial ecosystems. Our findings of how such contexts are created can help researchers understand how individual entrepreneurial learning can transform into joint learning, which benefits communities rather than just individuals (Pugh et al., 2021). As we outline, the contexts can take very supportive and caring dimensions, as seen in the case of “care-spaces” where entrepreneurs can share failure and receive support. These insights may provide an impetus for future research into where entrepreneurs turn to cope with failure (Cope, 2011; Simmons et al., 2014).

Although there has been a recent surge in research analyzing entrepreneurial learning as a socially embedded process (Hamilton, 2011; Pittaway et al., 2015; Pugh et al., 2021; Zozimo et al., 2017), Toutain et al. (2017, p. 883) argue that the entrepreneurial learning literature still lacks “a better understanding of the interactionist processes that play a role in the construction of social learning.” Our study improves understanding of how entrepreneurial learning is socially constructed in the following ways. First, we illustrate that entrepreneurial learning conversations in an online community can cover a broad range of topics that vary in emotionality and complexity. Thus, the interactionist processes that play a role in the construction of social learning should be understood as different processes. They vary in topic and, importantly, in emotionality, complexity, and abstraction. The implication of such difference is that learning in a community is defined not just by the type of community but also by topic. For example, a family business is a specific type of community (Hamilton, 2011), yet learning in a family business is also defined by topic. Learning about continuity in a family business is likely an emotional topic, due to the socioemotional wealth present in the business (Konopaski et al., 2015). Learning about marketing in the same family business may not be emotional, but perhaps an issue characterized by complexity. Therefore, scholars should not just differentiate entrepreneurial learning by what community it takes place in, but what the learning effort is about. In doing so, scholars may open for more fine-grained research on entrepreneurial learning in communities.

Second, we outline how different learning dynamics unfold in different conversations in the online community. In our findings, the “lessons learned” and the “advice-seeking” conversations have what we term a mentor-student dynamic. The mentor-student dynamic differs from the similar vicarious learning dynamics identified in previous research by having a more active student role (c.f. Hamilton, 2011; Konopaski et al., 2015; Zozimo et al., 2017). In the “lessons learned” conversations, the students actively engage with the mentor and ask questions and contrast with own experience, and sometimes they seek to disprove the mentor’s claims. In the “advice-seeking” conversations, the students start the conversation by actively seeking advice. Previous research has tended to portray students as passive following classic vicarious learning theory, e.g., a daughter hanging out in business premises (Konopaski et al., 2015, p. 359). Our findings indicate that students play a more active role and that learning is not necessarily unidirectional; students can challenge the mentor, provide analysis of the experience shared, and also contrast it with their own experience. Moreover, we also portray a different learning dynamic. In our “reflection” conversations, the dynamic is more between equal friends and supporters and not an unequal relation between a mentor and a student. The finding contrasts with previous research, which assumes power differences between a mentor and a student (e.g., Zozimo et al., 2017). In sum, our findings indicate that entrepreneurial learning in online communities is nuanced; it covers different levels of emotionality and complexity, and it also includes different dynamics.

By integrating research on entrepreneurial learning (Hamilton, 2011; Pittaway et al., 2015; Pugh et al., 2021; Wang & Chugh, 2014) and coactive vicarious learning (Myers, 2018) in the empirical context of online communities (Faraj et al., 2016), we provide novel insights into how entrepreneurial learning processes unfold, making three contributions. First, we extend the young stream of research investigating entrepreneurial learning in communities (Hamilton, 2011; Pittaway et al., 2015; Pugh et al., 2021) by providing novel insights into how entrepreneurs learn as part of online communities. In contrast to previous research, which has investigated how entrepreneurs learn through personal engagement in small, local communities, e.g., a daughter observing her father running the family business (Konopaski et al., 2015), we outline how entrepreneurs learn through entrepreneurial learning conversations in a global online community. In these conversations, entrepreneurial learning is socially constructed as entrepreneurs share experiences and insights, analyze them, debate them, and provide emotional support in sensitive matters. By drawing on coactive vicarious learning theory, we illustrate how entrepreneurial learning is achieved through active, discursive interactions in contrast to classic vicarious learning that limits entrepreneurial learning to passive observation (Myers, 2018, 2020). Our findings indicate that these conversations can help entrepreneurs both in mundane matters and with higher level learning as particular conversations may trigger critical reflection (Cope, 2003). Our focus on conversational learning is well grounded in the learning literature (c.f. Baker et al., 2005; Kolb & Kolb, 2005; Myers, 2018) but has not yet been applied to the entrepreneurial learning literature. Our paper may therefore be a steppingstone for future research to fully realize the potential of studying interactionist processes that create entrepreneurial learning (Toutain et al., 2017).

Second, we contribute to the growing interest in how vicarious learning takes place in online communities (Faraj et al., 2011, 2016) and how knowledge is constructed and shared (Faraj et al., 2016). Building on Myers’ (2018) theory to analyze knowledge sharing and learning as conversations, we uncover how the emotional depth and construal level of the discussions (Trope & Liberman, 2010) create micro-learning contexts, namely classroom context, collab context, club context, and care context that influence learning among entrepreneurs, thus underlining the importance of online communities as learning spaces for entrepreneurs. Thereby, we extend knowledge on learning spaces in online communities (Faraj et al., 2016). By enriching the understanding of the heterogeneity of digital learning spaces, our model of multiple micro-learning contexts further improves understanding of online communities as social spaces, which consist of different social practices (Levina & Arriaga, 2014).

Third, we translate Myers’ (2018) organizational theory of coactive vicarious learning into the context of online communities and provide empirical evidence of coactive vicarious learning. We extend his theory by nuancing the support and analysis dimensions, adding new insights into how emotionality and construal levels construct micro-learning contexts. These findings add to understanding the importance of the learning context, as highlighted in both experiential learning theory (Kolb & Kolb, 2005) and vicarious learning theory (Myers, 2018). Future research could further investigate the different micro-learning contexts that arise in communities. For example, studies of venture incubators may find different levels of successful learning due to different micro-learning contexts, thus shifting the focus further from “what” to “how” entrepreneurs learn.

Our study has several practical implications. First, our study emphasizes the growing importance for entrepreneurs to engage in online communities. As our findings indicate that taking an active part in learning conversations provides entrepreneurs with an important impetus for learning and can be especially beneficial for entrepreneurs with a lack of access to traditional entrepreneurship communities. Similarly, entrepreneurs, who may be isolated without close colleagues, can find help online. Second, our study might be of interest to entrepreneurship educators outside the traditional higher education context. Engagement with entrepreneurs outside a higher-education setting is underexplored, yet a clear opportunity for entrepreneurship education (Nabi et al., 2017). Online communities provide an exciting opportunity to engage with entrepreneurs from various contexts and to “democratize” entrepreneurship education. Third, our findings indicate the importance of creating various learning contexts to teach the various shades of entrepreneurship, from more tool-based approaches toward understanding team dynamics. Our findings suggest that different topics might thrive better in a specific micro-learning context. We thus encourage educators and organizers of entrepreneurship events to focus on the learning context they create. In light of the COVID-19 pandemic and crisis, these implications are especially important. Entrepreneurs are facing an unprecedented crisis in which they require all the help they can get. As we revisit the online community on Reddit, we note that how to deal with the COVID-19 crisis has become a primary focus of entrepreneurs. Thus, online communities may become a contingent resource during times of crisis, in which quick reactions and support matter.

As an inductive study on a new phenomenon, our study is not without limitations. First, we rely on data from one online community on Reddit. Although it is a large community with more than 380,000 members, future research should investigate the extent to which our findings are transferable across different online communities. Second, while our analysis considers how conversations play out, we do not investigate specifically how different trends or types of entrepreneurial learning conversations change over time, which opens up future research opportunities for more process-driven studies. Third, by taking a social constructionist approach through which we focus on how learning is created through conversations, we are limiting our focus on the individual, cognitive side of learning, e.g., how entrepreneurs transform experience into knowledge. For future research, it may be prudent to pursue an experiential learning perspective (Kolb, 1984) to complement our coactive vicarious perspective. Fourth, while we provide insights into entrepreneurial learning conversations, we cannot show specific learning outcomes. This is a result of our methodology and choice of an inductive research question; we thus encourage future research to test learning outcomes of entrepreneurs in online communities. Last, while our analysis is based upon the conversations among entrepreneurs, in line with other research on online communities (McKenna et al., 2017), we encourage future research to additionally collect other types of qualitative data, such as interviews or diary studies, to further triangulate the findings.